Investigating the Role of Mitochondrial Toxicity in Drug-Induced Liver Injury via

Congruent Structure-Activity Relationships

Mukesh PatelLhasa Limited

mukesh.patel@lhasalimited.org

Overview

Introduction

Project outline

Strategy

Results

Discussion

Mitochondrial toxicity

Mechanisms of mitochondrial toxicity

Mitochondrial toxicity

Mechanisms of mitochondrial toxicity

Hepatotoxicity

Cardiotoxicity

Why predict mitochondrial toxicity?

• Underlying effects on mitochondria may manifest as toxicity at the organ level

• One of the proposed mechanisms for idiosyncratic toxicity

• Can existing alerts for organ toxicity (liver) be used to identify structure-activity relationships for mitochondrial dysfunction?

Project outline

Collaboration between Lhasa Limited andPfizer

• Investigate structure-activity relationships (SARs)for mitochondrial dysfunction induced bychemicals

• Analyse data sets available in the publishedliterature for potential SARs for mitochondrialdysfunction

• Investigate ability of an existing knowledge baseof structural alerts for hepatotoxicity in predictingmitochondrial dysfunction

• Implement SARs as prototype alerts

Strategy

Data set

New alert

No actionHepatotoxicitykb

InvestigatePrediction

No prediction

Incorrect

Correct

Investigate

Rationale

Suitable alertto duplicate

Investigate

Rationale

Data sets used

A. Mehta R. et al. Chapter 3, Drug-associated mitochondrial toxicity. Drug-inducedmitochondrial dysfunction. Dykens JA, Will Y, (editors), Oxford: Wiley-Blackwell,2008, 71-126.

B. Zhang H. et al. In silico prediction of mitochondrial toxicity by using GA-CG-SVMapproach. Toxicology in Vitro. 2009, 23(1), 134-140.

C. Wagner BK. et al. Large-scale chemical dissection of mitochondrial function. NatureBiotechnology. 2008, 26(3), 343-351.

Data set Type of data Source of data

A In vivo Collated from published reports

B In vivo Collated from published reports

C In vitro Cell based assay data

Data set composition

A. Mehta R. et al. Chapter 3, Drug-associated mitochondrial toxicity. Drug-inducedmitochondrial dysfunction. Dykens JA, Will Y, (editors), Oxford: Wiley-Blackwell,2008, 71-126.

B. Zhang H. et al. In silico prediction of mitochondrial toxicity by using GA-CG-SVMapproach. Toxicology in Vitro. 2009, 23(1), 134-140.

C. Wagner BK. et al. Large-scale chemical dissection of mitochondrial function. NatureBiotechnology. 2008, 26(3), 343-351.

Data set Total no of compounds

No of positives

No of negatives

A 105 105 -

B 282 166 116

C 2490 101 2389

Hepatotoxicity alerts

Knowledge base of 62 structural alerts for hepatotoxicity

• Implemented in an expert system for predictive toxicity (Derek for Windows [DfW v12])

Developed from published data sources

• Mix of ‘pharmaceutical’ and ‘industrial’ chemicals

• Human and Animal in vivo data

Platform presentation ‘Developing Knowledge of Structure-Activity Relationships in Toxicity.’ R Note. SOT, 2006.

Greene et al. Chemical Research and Toxicology, 2010, 23, 1215-1222.

Marchant et al. Chemistry and Biodiversity, 2009, 6, 2107–2114.

Strategy

Data set

New alert

No actionDfW v12 InvestigatePrediction

No prediction

Incorrect

Correct

Investigate

Rationale

Suitable alertto duplicate

Investigate

Rationale

Results

Data set A B C

Sensitivity (%) 62 43 28

Specificity (%) n/a 72 67

Concordance (%) 62 55 65

Positive predictivity (%) 100 68 4

52/62 hepatotoxicity alerts triggered for at least one compound in at least one data set

Sensitivity = Proportion of active compounds correctly predictedSpecificity = Proportion of inactive compounds correctly predictedConcordance = Proportion of all correct predictionsPositive predictivity = Proportion of correct positive predictions

ResultsPositive predictivity per hepatotoxicity alert

PP = TP/(TP + FP)

Alert number Alert

Dataset

A B C

81 Aliphatic nitro compound na na 0

202 Pyrroline or pyrrole ester na na 0

538 Tetracycline 100 100 0

539 Thiazolidinedione 100 100 0

540 Statin na 100 0

541 Erythromycin na na 0

542 Furan 100 0 4

543 Thiophene 100 0 6

544 Aromatic nitro compound 100 100 2

545 Amiodarone or analogue 100 100 0

546 Short chain fatty acids 100 89 4

547 2,4-Dihalophenylnaimidazole or analogue 100 na 0

548 2-Mercaptoimidazole na na 0

549 Phenytoin-like compound 100 100 0

550 Benzodiazepine or analogue 0 100 0

551 Salicylic acid or analogue 100 100 0

552 Bile acid derivative 100 100 0

553 Formamide derivative na 100 0

554 Retinoid or analogue na 0 0

558 Dibenzodiazepine or analogue 100 100 0

559 Arsenic compound na na 0

611 Phenothiazine or thioxanthene derivative 100 29 4

612 4-Aminophenylsulphonamide or 4-aminophenylsulphone na na 0

613 Arylsulphonylurea or analogue 100 na 0

614 N-Acyl-pyrrolidine, -piperidine or analogue na 0 0

615 Hydrazine 100 100 0

616 Halothane or analogue 100 0 0

617 Halogenated hydrocarbon 100 100 12

618 Steroid 0 0 0

619 para-Aminophenol or derivative 100 100 0

620 2-Arylacetic or 3-arylpropionic acid 100 71 0

632 Anthraquinone derivative 100 100 28

649 Organic peroxide na na 0

651 2,4-Diamino-1,3,5-triazine na na 0

653 Organophosphorus di- or tri-ester na 100 0

665 Azoxy compound na 100 0

666 para-Alkylphenol or derivative 100 18 8

P059 1,2-Diphenyl-3,5-pyrazolidinedione derivative na na 0

P060 beta-Lactam with penicillin or cephalosporin ring 100 na 0

P061 N-Phenylanthranilic acid or derivative 100 100 0

P062 Salicylic acid or analogue 100 100 0

P063 Phenylethylamine or derivative na 25 18

P065 Quinoline 100 100 4

P066 Quinolone-3-carboxylic acid or naphthyridine analogue 100 100 0

P068 4-Hydroxycoumarin derivative na 0 0

P069 Purine base 100 100 11

P070 Phenoxyacetic acid or derivative 100 100 0

P071 Phenyl or thiadiazole sulphonamide na 100 0

P072 Carbamate 100 100 3

P074 N-Nitrosourea derivative na na 0

P075 Phenyl hydantoin, phenyl barbiturate, or phenylacetylurea derivative 100 100 0

P076 Benzimidazole or derivative na 0 6

ResultsAlert performance against data set A

Abbreviation Effect

DIFL/SH Steatosis

DIHC Cholestasis

C1 ….C5 Inhibition of complex n

MtDNA Effect on Mitochondrial DNA

IND Other mechanism

Strategy

Data set

9

No actionDfW12 InvestigatePrediction

No prediction

Incorrect

Correct

Investigate

Rationale

15

Investigate

Rationale

New mitochondrial dysfunctionprototype alerts

•Biguanide•Aminoacetanilide•Tropolone•Phenol•Benzamidine•1,4-Benzoquinone•Quaternary ammonium cation•Nitrogen or sulphur mustard•Carbonyl cyanide phenylhydrazone

New mitochondrial dysfunctionprototype alerts

buformin metformin phenformin

E.g. Biguanide derivatives

Mechanism – Inhibition of complex 1 on the electron transport chain

NH2

N

NH

NH

N

NH2

N

N NH

N

NH2

N

NH

NH

N

Suitable hepatotoxicity classes for prototype alerts

Tetracyclines

1. Inhibition of mitochondrial beta-oxidation

Thiazolidinediones

1. Inhibitors of complex 1

Short chain fatty acids

1. Impairment of the beta-oxidation of natural fatty acids

2. Irreversible deactivation of mitochondrial enzymes

O

O

N

N

SO

O

O

O

O

N

OO

OOO

ON

Unsuitable hepatotoxicity classes for prototype alerts ?

Thiophene examples used to derive hepatotoxicity alert

Thiophene examples from data set A

S

OH

O

S

O Cl

Cl

O

N

N

N

S

S

N

Cl

N

S O

S

N+

O

O O

O

O

S

N

N

S

O

O O

O

New prototype alert predictions against data set A

Abbreviation Effect

DIFL/SH Steatosis

DIHC Cholestasis

C1 ….C5 Inhibition of complex n

MtDNA Effect on Mitochondrial DNA

IND Other mechanism

Conclusions

9 new prototype alerts for the mitochondrial dysfunction endpoint were developed based on compounds with mitochondrial liabilities

15 prototype alerts developed from existing hepatotoxicity alerts

Common mechanism of action for some classes

Elucidating mechanisms of sub cellular damage help in the prediction and understanding of potential toxicity at the organ level

Acknowledgements

Lhasa

• Lilia Fisk

• Diana Suarez

• Reine Note

Pfizer

• Nigel Greene

• Russ Naven

• Dennis Pelletier